Are milk quota prices a rational investment? Modeling quotas as financial assets
Bibliographic record
Abstract
Abstract Our objective in this paper is to examine the growth of the price of farm milk quotas in Canada, to shed light on their patterns of growth within the last 15 years. Our quota price model is based on the Gordon growth model, supplemented by some important characteristics of the milk quota market. Our explanatory variables include the quota rental price, interest rate, and expectations of both price and quantity growth, all for the province of Alberta over the 2009–2023 time period. Our economic model of quota prices performs well by giving results with the predicted signs for all variables in our model and high levels of statistical significance except for expectations of quantity allocations when entered separately. The model has two variants, one with the expectation of growth in quota rents and the other with the expectation of growth in the quota asset price. Econometric issues related to time series problems are dealt with. The model allows calculation of policy risk, which shows an interesting pattern over this time period.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".